Project title: Optimization of empirical potentials by AI methods

Description: Development of a computational technique to optimize the parameters of empirical potentials using machine learning. Applicant should write the code allowing the optimization of parameters of empirical potential which will describe the crystal structure. This potential will then be used to run real simulations of new compound and to determine its properties. Results of simulations can be included in the publication in the journal (if successful)

Candidates requirements:
➔ knowledge of programming in python
➔ skills in machine learning
➔ ability to work in a team
➔ basic knowledge in atomistic simulations (not necessary)
➔ desire to learn and work

Supervisor: Full Professor Alexander Kvashnin

Internship duration: from 4 to 12 months

The start date of the internship: as soon as possible

Monthly compensation: not available

Contact person: Alexander Kvashnin a.kvashnin@skoltech.ru